Marco A. Acevedo Zamora


Project Overview

This work will use Artificial Intelligence tools for polymetallic ore and gangue characterization that are promising for revolutionizing microscopic studies of thin/thick sections (Petrography), a common ground between Industry and Academia, benefiting geological studies and the society.

We want to develop new data acquisition (1st work package, WP1) and analysis tool software (2nd work package, WP2) to elaborate empirical models for predicting trace element behavior. The WP1 is scheduled for the end the project and include the development of Ultra-fast LA-ICP-MS analysis. The WP2 include developing software to process microscopy image Big Data from state-of-the-art analytical techniques, using Image Processing and combining their pixel information at an adequate resolution for representation and spectral study using a correlative microscopy approach.

By characterizing and modeling trace elements in a pilot software platform, we are looking forward to for increasing resource efficiency in the mining value chain. The methodology development objectives are be divided in two work packages.


Acevedo, M. (2016). Emplacement and Magmatic Evolution of the Val Fredda Complex intrusions (southern Adamello Batholith, N. Italy). MSc in Geology thesis archive, 177 p., University of Geneva  (UNIGE).

Conference Presentations

Acevedo, M. (2020). “Novel ways of automated trace element-mineral association recognition.” Manuscript accepted in Conference in Minerals Engineering, February 2020. Lulea, Sweden.

L.S. Jordán, E. Sánchez, M. Acevedo, M.C. Lázaro (2014). Controles estructurales en el distrito minero de Tintaya. XVII Congreso Peruano de Geología. Resúmenes extendidos, Boletín 109-14 (Sociedad Geológica del Perú).


Deep Learning specialization. Andrew Ng. at Coursera online (Aug 2020).

Machine Learning: Introduction to Artificial Intelligence in MatLab with Andrew Ng. (Stanford University) at Coursera online (Jan 2019)..


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